Protocol stack for analog communication in split architecture network for machine learning (ml) functions

ABSTRACT

A protocol stack architecture for processing machine learning (ML) data includes a ML layer to manage ML data communication with a network device. The ML layer is coupled to multiple ML training blocks, and ML and inference blocks for multiple neural networks, and an analog data communications stack coupled to the ML layer. The analog data communications stack has an upper media access control analog (MAC-A) layer coupled to the ML layer and configured to store data for each neural network, a lower MAC-A layer coupled to the upper MAC-A layer and configured to segment and reassemble analog ML data, and an analog physical layer coupled to the lower MAC-A layer and configured to communicate analog data with the network device. The architecture includes a digital data communications stack coupled to the ML layer and the lower MAC-A layer and configured to manage digital communications with the network device.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to wireless communications, andmore specifically to a protocol stack for analog transmission andreception in a split architecture network for machine learning (ML)functions.

BACKGROUND

Wireless communications systems are widely deployed to provide varioustelecommunications services such as telephony, video, data, messaging,and broadcasts. Typical wireless communications systems may employmultiple-access technologies capable of supporting communications withmultiple users by sharing available system resources (e.g., bandwidth,transmit power, and/or the like). Examples of such multiple-accesstechnologies include code division multiple access (CDMA) systems, timedivision multiple access (TDMA) systems, frequency-division multipleaccess (FDMA) systems, orthogonal frequency-division multiple access(OFDMA) systems, single-carrier frequency-division multiple access(SC-FDMA) systems, time division synchronous code division multipleaccess (TD-SCDMA) systems, and long term evolution (LTE).LTE/LTE-Advanced is a set of enhancements to the universal mobiletelecommunications system (UMTS) mobile standard promulgated by theThird Generation Partnership Project (3GPP). Narrowband (NB)-Internet ofthings (IoT) and enhanced machine-type communications (eMTC) are a setof enhancements to LTE for machine type communications.

A wireless communications network may include a number of base stations(BSs) that can support communications for a number of user equipment(UEs). A user equipment (UE) may communicate with a base station (BS)via the downlink and uplink. The downlink (or forward link) refers tothe communications link from the BS to the UE, and the uplink (orreverse link) refers to the communications link from the UE to the BS.As will be described in more detail, a BS may be referred to as a NodeB, an evolved Node B (eNB), a gNB, an access point (AP), a radio head, atransmit and receive point (TRP), a new radio (NR) BS, a 5G Node B,and/or the like.

The above multiple access technologies have been adopted in varioustelecommunications standards to provide a common protocol that enablesdifferent user equipment to communicate on a municipal, national,regional, and even global level. New Radio (NR), which may also bereferred to as 5G, is a set of enhancements to the LTE mobile standardpromulgated by the Third Generation Partnership Project (3GPP). NR isdesigned to better support mobile broadband Internet access by improvingspectral efficiency, lowering costs, improving services, making use ofnew spectrum, and better integrating with other open standards usingorthogonal frequency division multiplexing (OFDM) with a cyclic prefix(CP) (CP-OFDM) on the downlink (DL), using CP-OFDM and/or SC-FDM (e.g.,also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) onthe uplink (UL), as well as supporting beamforming, multiple-inputmultiple-output (MIMO) antenna technology, and carrier aggregation.

Artificial neural networks may comprise interconnected groups ofartificial neurons (e.g., neuron models). The artificial neural networkmay be a computational device or represented as a method to be performedby a computational device. Convolutional neural networks, such as deepconvolutional neural networks, are a type of feed-forward artificialneural network. Convolutional neural networks may include layers ofneurons that may be configured in a tiled receptive field. It would bedesirable to apply neural network processing to wireless communicationsto achieve greater efficiencies.

SUMMARY

Aspects of the present disclosure are directed to a protocol stackarchitecture for processing machine learning data at a user equipment(UE). The UE has a machine learning layer configured to managecommunication of machine learning data with a network device. Themachine learning layer is coupled to multiple machine learning trainingblocks, and machine learning and inference blocks for multiple neuralnetworks. The protocol stack architecture also has an analog datacommunications stack coupled to the machine learning layer. The analogdata communications stack has an upper media access control analog(MAC-A) layer coupled to the machine learning layer and configured tostore data for each of the neural networks. The analog datacommunications stack also has a lower MAC-A layer coupled to the upperMAC-A layer and configured to segment and reassemble analog machinelearning data. The analog data communications stack has an analogphysical layer coupled to the lower MAC-A layer and configured totransmit and receive analog data to and from the network device. Theprotocol stack architecture further has a digital data communicationsstack coupled to the machine learning layer and the lower MAC-A layerand configured to manage digital communications with the network device.

In other aspects of the present disclosure, a method of wirelesscommunication, by a user equipment (UE), includes receiving a controlmessage from a machine learning training block or machine learninginference block. The method also includes transmitting, to a networkdevice, the control message via a digital data communications stack. Themethod further includes receiving gradient data for federated learning,from the machine learning training block or the machine learninginference block. The method still further includes determining whetherto transmit the gradient data via an analog data communications stack orthe digital data communications stack based on a network configuration.The method also includes transmitting, to the network device, thegradient data via the analog data communications stack or the digitaldata communications stack in accordance with the determining.

Other aspects of the present disclosure are directed to a protocol stackarchitecture for processing machine learning data at a network devicehaving a machine learning layer configured to manage communication ofmachine learning data with a user equipment (UE). The machine learninglayer is coupled to multiple machine learning training blocks, andmachine learning and inference blocks for multiple neural networks. Theprotocol stack architecture also has an analog data communications stackcoupled to the machine learning layer. The analog data communicationsstack has an upper media access control analog (MAC-A) layer coupled tothe machine learning layer and configured to store data for each of theneural networks. The analog data communications stack also has a lowerMAC-A layer coupled to the upper MAC-A layer and configured to segmentand reassemble analog machine learning data. The analog datacommunications stack further has an analog physical layer coupled to thelower MAC-A layer and configured to transmit and receive analog data toand from the network device. The protocol stack architecture further hasa digital data communications stack coupled to the machine learninglayer and the lower MAC-A layer and configured to manage digitalcommunications with the UE.

In other aspects of the present disclosure, a method of wirelesscommunication, by a network device, includes receiving a control messagefrom a machine learning training block or machine learning inferenceblock. The method also includes transmitting the control message via adigital data communications stack. The method further includes receivinggradient data for federated learning, from the machine learning trainingblock or the machine learning inference block. The method still furtherincludes determining whether to transmit the gradient data via an analogdata communications stack or the digital data communications stack basedon a network configuration. The method also includes transmitting thegradient data via the analog data communications stack or the digitaldata communications stack in accordance with the determining.

Aspects generally include a method, apparatus, system, computer programproduct, non-transitory computer-readable medium, user equipment, basestation, wireless communication device, and processing system assubstantially described with reference to and as illustrated by theaccompanying drawings and specification.

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to the disclosure in order that thedetailed description that follows may be better understood. Additionalfeatures and advantages will be described. The conception and specificexamples disclosed may be readily utilized as a basis for modifying ordesigning other structures for carrying out the same purposes of thepresent disclosure. Such equivalent constructions do not depart from thescope of the appended claims. Characteristics of the concepts disclosed,both their organization and method of operation, together withassociated advantages will be better understood from the followingdescription when considered in connection with the accompanying figures.Each of the figures is provided for the purposes of illustration anddescription, and not as a definition of the limits of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

So that features of the present disclosure can be understood in detail,a particular description may be had by reference to aspects, some ofwhich are illustrated in the appended drawings. It is to be noted,however, that the appended drawings illustrate only certain aspects ofthis disclosure and are therefore not to be considered limiting of itsscope, for the description may admit to other equally effective aspects.The same reference numbers in different drawings may identify the sameor similar elements.

FIG. 1 is a block diagram conceptually illustrating an example of awireless communications network, in accordance with various aspects ofthe present disclosure.

FIG. 2 is a block diagram conceptually illustrating an example of a basestation in communication with a user equipment (UE) in a wirelesscommunications network, in accordance with various aspects of thepresent disclosure.

FIG. 3 illustrates an example implementation of designing a neuralnetwork using a system-on-a-chip (SOC), including a general-purposeprocessor, in accordance with certain aspects of the present disclosure.

FIGS. 4A, 4B, and 4C are diagrams illustrating a neural network, inaccordance with aspects of the present disclosure.

FIG. 4D is a diagram illustrating an exemplary deep convolutionalnetwork (DCN), in accordance with aspects of the present disclosure.

FIG. 5 is a block diagram illustrating an exemplary deep convolutionalnetwork (DCN), in accordance with aspects of the present disclosure.

FIG. 6 is a block diagram illustrating over the air aggregation forfederated learning, in accordance with aspects of the presentdisclosure.

FIG. 7 is a block diagram illustrating a network architecture, inaccordance with aspects of the present disclosure.

FIG. 8 is a block diagram illustrating distribution of a neural networkacross various network elements, in accordance with aspects of thepresent disclosure.

FIG. 9 is a block diagram illustrating an architecture for a networkmanaged machine learning model of a device, in accordance with aspectsof the present disclosure.

FIG. 10 is a block diagram illustrating a protocol stack for processingmachine learning data, in accordance with aspects of the presentdisclosure.

FIG. 11 is a call flow diagram illustrating network-initiated gradienttransmission, in accordance with aspects of the present disclosure.

FIG. 12A is a call flow diagram illustrating network-initiated datasample availability reporting, in accordance with aspects of the presentdisclosure.

FIG. 12B is a call flow diagram illustrating user equipment(UE)-initiated data sample availability reporting, in accordance withaspects of the present disclosure.

FIG. 13 is a call flow diagram illustrating continuous gradientcomputing at a user equipment (UE), in accordance with aspects of thepresent disclosure.

FIG. 14 is a block diagram illustrating a data packet format forgradient transmissions, in accordance with aspects of the presentdisclosure.

FIG. 15 is a block diagram illustrating PHY layer repetition withdifferent antenna ports, in accordance with aspects of the presentdisclosure.

FIG. 16 is a block diagram illustrating PHY layer repetition withdifferent interleaving, in accordance with aspects of the presentdisclosure.

FIG. 17 is a flow diagram illustrating an example process performed, forexample, by a user equipment (UE), in accordance with various aspects ofthe present disclosure.

FIG. 18 is a flow diagram illustrating an example process performed, forexample, by a network device, in accordance with various aspects of thepresent disclosure.

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully below withreference to the accompanying drawings. This disclosure may, however, beembodied in many different forms and should not be construed as limitedto any specific structure or function presented throughout thisdisclosure. Rather, these aspects are provided so that this disclosurewill be thorough and complete, and will fully convey the scope of thedisclosure to those skilled in the art. Based on the teachings, oneskilled in the art should appreciate that the scope of the disclosure isintended to cover any aspect of the disclosure, whether implementedindependently of or combined with any other aspect of the disclosure.For example, an apparatus may be implemented or a method may bepracticed using any number of the aspects set forth. In addition, thescope of the disclosure is intended to cover such an apparatus ormethod, which is practiced using other structure, functionality, orstructure and functionality in addition to or other than the variousaspects of the disclosure set forth. It should be understood that anyaspect of the disclosure disclosed may be embodied by one or moreelements of a claim.

Several aspects of telecommunications systems will now be presented withreference to various apparatuses and techniques. These apparatuses andtechniques will be described in the following detailed description andillustrated in the accompanying drawings by various blocks, modules,components, circuits, steps, processes, algorithms, and/or the like(collectively referred to as “elements”). These elements may beimplemented using hardware, software, or combinations thereof. Whethersuch elements are implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem.

It should be noted that while aspects may be described using terminologycommonly associated with 5G and later wireless technologies, aspects ofthe present disclosure can be applied in other generation-basedcommunications systems, such as and including 3G and/or 4G technologies.

Some machine learning approaches centralize training data on onemachine, or in a data center. A federated learning model supportscollaborative learning of a shared prediction model among user equipment(UEs) and a base station (or centralized server). Federated learning isa process where a group of UEs receives a machine learning model from abase station and work together to train the model. More specifically,each UE trains the model locally, and sends back either updated neuralnetwork model weights or gradient updates from, for example, a locallyperformed stochastic gradient descent process. The base station receivesthe updates from all of the UEs in the group and aggregates the updates,for example, by averaging the updates, to obtain updated global weightsof the neural network. The base station sends the updated model to theUEs, and the process repeats, round after round, until a desiredperformance level from the global model is obtained.

Over the air (OTA) aggregation for federated learning is an attractiveapproach due to its low communication overhead. With OTA aggregation,each UE transmits the gradient of the weights to the network (e.g.,parameter server). The network then aggregates the gradients from all ofthe UEs according to a function (e.g., a summation). Because wirelesssignals naturally add up on the uplink, it is beneficial to transmit thegradient of each parameter on the uplink as analog data for each usersimultaneously. Thus, the base station (e.g., gNB) receives the sum ofthe gradients in the ideal scenario. However, considerations such aspower control, fading compensation, phase correction, etc., need to beaddressed so that the base station properly adds the gradients.

With federated learning techniques, different sets of analog gradientdata may be fetched at different times. It would be desirable tointroduce a framework for realizing the federated learning techniqueswith a new radio (NR) network. Currently, the NR network is designed toprocess data packets and not analog inputs from the physical layer(PHY). Aspects of the present disclosure introduce an architecturalenhancement to the NR network to enable the network to compute uplinkchannel-based analog gradient sums. The protocol architecture includesan analog protocol stack and a digital protocol stack (also referred toas analog data communications stack and digital data communicationsstack, respectively). The analog protocol stack may transport analogdata such as machine learning model gradients or weights.

The protocol stack includes multiple layers that communicate with eachother. The layers may be software or hardware-based. Each includes aprotocol stack, as does each base station. A machine learning (ML) layeris present in the protocol stacks of the UE, the base station, and thecore network. The ML layer may be tasked with managing machine learningdata transmission and reception needs of the network entities fortransmissions to and from the base station, the UE, and the corenetwork. An interface exists between machine learning training andinference blocks, data management and control blocks, etc. at one end ofthe ML layer. An interface also exists between the ML layer and the UEand base station transmission and reception stacks at the other end. Thecore network includes an ML layer that communicates with the ML layer ofthe base station to receive the gradient data.

According to aspects of the present disclosure, the ML layer may beconfigured to transmit the data it handles in either an analog or adigital format. For the digital format, the data passes through theregular UE digital data stack. For the analog format, the data passesthrough a simplified UE analog stack containing an upper analog MAC(MAC-A) layer, a lower analog MAC layer, and an analog physical (PHY-A)layer. The functions of these layers may be performed by the regular UEdata stack or a separate analog stack.

According to aspects of the present disclosure, based on either a priorconfiguration or a dynamic indication, the ML layer can choose totransmit data in either digital or analog format. The decision may be apacket-by-packet determination of a flow-by-flow determination. In someaspects, all control messages may be transmitted using the digital datastack. Gradient data to be transmitted for the purpose of federatedlearning may be transmitted in either the analog format or the digitalformat. If the network determines that there are a limited number of UEscommunicating with a particular base station participating in thefederated learning session, the network may decide it is more efficientto transmit gradient data in the digital domain. Alternatively, if thenetwork determines that a large number of UEs are participating in thefederated learning session, then the network may decide it is moreefficient to transmit gradient data in the analog domain. In case thefunctions of the analog layers are performed by the regular UE datastack, the ML layer communicates with the regular UE digital data stackwith an indication of whether the analog or digital functions should beperformed either packet-by-packet or flow-by-flow.

FIG. 1 is a diagram illustrating a network 100 in which aspects of thepresent disclosure may be practiced. The network 100 may be a 5G or NRnetwork or some other wireless network, such as an LTE network. Thewireless network 100 may include a number of BSs 110 (shown as BS 110 a,BS 110 b, BS 110 c, and BS 110 d) and other network entities. A BS is anentity that communicates with user equipment (UEs) and may also bereferred to as a base station, a NR BS, a Node B, a gNB, a 5G node B, anaccess point, a transmit and receive point (TRP), a network node, anetwork entity, and/or the like. A base station can be implemented as anaggregated base station, as a disaggregated base station, an integratedaccess and backhaul (IAB) node, a relay node, a sidelink node, etc. Thebase station can be implemented in an aggregated or monolithic basestation architecture, or alternatively, in a disaggregated base stationarchitecture, and may include one or more of a central unit (CU), adistributed unit (DU), a radio unit (RU), a near-real time (near-RT) RANintelligent controller (RIC), or a non-real time (non-RT) RIC. Each BSmay provide communications coverage for a particular geographic area. In3GPP, the term “cell” can refer to a coverage area of a BS and/or a BSsubsystem serving this coverage area, depending on the context in whichthe term is used.

A BS may provide communications coverage for a macro cell, a pico cell,a femto cell, and/or another type of cell. A macro cell may cover arelatively large geographic area (e.g., several kilometers in radius)and may allow unrestricted access by UEs with service subscription. Apico cell may cover a relatively small geographic area and may allowunrestricted access by UEs with service subscription. A femto cell maycover a relatively small geographic area (e.g., a home) and may allowrestricted access by UEs having association with the femto cell (e.g.,UEs in a closed subscriber group (CSG)). A BS for a macro cell may bereferred to as a macro BS. A BS for a pico cell may be referred to as apico BS. A BS for a femto cell may be referred to as a femto BS or ahome BS. In the example shown in FIG. 1 , a BS 110 a may be a macro BSfor a macro cell 102 a, a BS 110 b may be a pico BS for a pico cell 102b, and a BS 110 c may be a femto BS for a femto cell 102 c. A BS maysupport one or multiple (e.g., three) cells. The terms “eNB,” “basestation,” “NR BS,” “gNB,” “AP,” “node B,” “5G NB,” “TRP,” and “cell” maybe used interchangeably.

In some aspects, a cell may not necessarily be stationary, and thegeographic area of the cell may move according to the location of amobile BS. In some aspects, the BSs may be interconnected to one anotherand/or to one or more other BSs or network nodes (not shown) in thewireless network 100 through various types of backhaul interfaces suchas a direct physical connection, a virtual network, and/or the likeusing any suitable transport network.

The wireless network 100 may also include relay stations. A relaystation is an entity that can receive a transmission of data from anupstream station (e.g., a BS or a UE) and send a transmission of thedata to a downstream station (e.g., a UE or a BS). A relay station mayalso be a UE that can relay transmissions for other UEs. In the exampleshown in FIG. 1 , a relay station 110 d may communicate with macro BS110 a and a UE 120 d in order to facilitate communications between theBS 110 a and UE 120 d. A relay station may also be referred to as arelay BS, a relay base station, a relay, and/or the like.

The wireless network 100 may be a heterogeneous network that includesBSs of different types, e.g., macro BSs, pico BSs, femto BSs, relay BSs,and/or the like. These different types of BSs may have differenttransmit power levels, different coverage areas, and different impact oninterference in the wireless network 100. For example, macro BSs mayhave a high transmit power level (e.g., 5 to 40 Watts) whereas pico BSs,femto BSs, and relay BSs may have lower transmit power levels (e.g., 0.1to 2 Watts).

A core network 130 may couple to a set of BSs and may providecoordination and control for these BSs. The core network 130 maycommunicate with the BSs via a backhaul. The BSs may also communicatewith one another, e.g., directly or indirectly via a wireless orwireline backhaul.

UEs 120 (e.g., 120 a, 120 b, 120 c) may be dispersed throughout thewireless network 100, and each UE may be stationary or mobile. A UE mayalso be referred to as an access terminal, a terminal, a mobile station,a subscriber unit, a station, and/or the like. A UE may be a cellularphone (e.g., a smart phone), a personal digital assistant (PDA), awireless modem, a wireless communications device, a handheld device, alaptop computer, a cordless phone, a wireless local loop (WLL) station,a tablet, a camera, a gaming device, a netbook, a smartbook, anultrabook, a medical device or equipment, biometric sensors/devices,wearable devices (smart watches, smart clothing, smart glasses, smartwrist bands, smart jewelry (e.g., smart ring, smart bracelet)), anentertainment device (e.g., a music or video device, or a satelliteradio), a vehicular component or sensor, smart meters/sensors,industrial manufacturing equipment, a global positioning system device,or any other suitable device that is configured to communicate via awireless or wired medium.

Some UEs may be considered machine-type communications (MTC) or evolvedor enhanced machine-type communications (eMTC) UEs. MTC and eMTC UEsinclude, for example, robots, drones, remote devices, sensors, meters,monitors, location tags, and/or the like, that may communicate with abase station, another device (e.g., remote device), or some otherentity. A wireless node may provide, for example, connectivity for or toa network (e.g., a wide area network such as Internet or a cellularnetwork) via a wired or wireless communications link. Some UEs may beconsidered Internet-of-Things (IoT) devices, and/or may be implementedas NB-IoT (narrowband internet of things) devices. Some UEs may beconsidered a customer premises equipment (CPE). UE 120 may be includedinside a housing that houses components of UE 120, such as processorcomponents, memory components, and/or the like.

In general, any number of wireless networks may be deployed in a givengeographic area. Each wireless network may support a particular RAT andmay operate on one or more frequencies. A RAT may also be referred to asa radio technology, an air interface, and/or the like. A frequency mayalso be referred to as a carrier, a frequency channel, and/or the like.Each frequency may support a single RAT in a given geographic area inorder to avoid interference between wireless networks of different RATs.In some cases, NR or 5G RAT networks may be deployed.

In some aspects, two or more UEs 120 (e.g., shown as UE 120 a and UE 120e) may communicate directly using one or more sidelink channels (e.g.,without using a base station 110 as an intermediary to communicate withone another). For example, the UEs 120 may communicate usingpeer-to-peer (P2P) communications, device-to-device (D2D)communications, a vehicle-to-everything (V2X) protocol (e.g., which mayinclude a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure(V2I) protocol, and/or the like), a mesh network, and/or the like. Inthis case, the UE 120 may perform scheduling operations, resourceselection operations, and/or other operations described elsewhere asbeing performed by the base station 110. For example, the base station110 may configure a UE 120 via downlink control information (DCI), radioresource control (RRC) signaling, a media access control-control element(MAC-CE) or via system information (e.g., a system information block(SIB).

As indicated above, FIG. 1 is provided merely as an example. Otherexamples may differ from what is described with regard to FIG. 1 .

FIG. 2 shows a block diagram of a design 200 of the base station 110 andUE 120, which may be one of the base stations and one of the UEs in FIG.1 . The base station 110 may be equipped with T antennas 234 a through234 t, and UE 120 may be equipped with R antennas 252 a through 252 r,where in general T ≥ 1 and R ≥ 1.

At the base station 110, a transmit processor 220 may receive data froma data source 212 for one or more UEs, select one or more modulation andcoding schemes (MCS) for each UE based at least in part on channelquality indicators (CQIs) received from the UE, process (e.g., encodeand modulate) the data for each UE based at least in part on the MCS(s)selected for the UE, and provide data symbols for all UEs. Decreasingthe MCS lowers throughput but increases reliability of the transmission.The transmit processor 220 may also process system information (e.g.,for semi-static resource partitioning information (SRPI) and/or thelike) and control information (e.g., CQI requests, grants, upper layersignaling, and/or the like) and provide overhead symbols and controlsymbols. The transmit processor 220 may also generate reference symbolsfor reference signals (e.g., the cell-specific reference signal (CRS))and synchronization signals (e.g., the primary synchronization signal(PSS) and secondary synchronization signal (SSS)). A transmit (TX)multiple-input multiple-output (MIMO) processor 230 may perform spatialprocessing (e.g., precoding) on the data symbols, the control symbols,the overhead symbols, and/or the reference symbols, if applicable, andmay provide T output symbol streams to T modulators (MODs) 232 a through232 t. Each modulator 232 may process a respective output symbol stream(e.g., for orthogonal frequency division multiplexing (OFDM) and/or thelike) to obtain an output sample stream. Each modulator 232 may furtherprocess (e.g., convert to analog, amplify, filter, and upconvert) theoutput sample stream to obtain a downlink signal. T downlink signalsfrom modulators 232 a through 232 t may be transmitted via T antennas234 a through 234 t, respectively. According to various aspectsdescribed in more detail below, the synchronization signals can begenerated with location encoding to convey additional information.

At the UE 120, antennas 252 a through 252 r may receive the downlinksignals from the base station 110 and/or other base stations and mayprovide received signals to demodulators (DEMODs) 254 a through 254 r,respectively. Each demodulator 254 may condition (e.g., filter, amplify,downconvert, and digitize) a received signal to obtain input samples.Each demodulator 254 may further process the input samples (e.g., forOFDM and/or the like) to obtain received symbols. A MIMO detector 256may obtain received symbols from all R demodulators 254 a through 254 r,perform MIMO detection on the received symbols if applicable, andprovide detected symbols. A receive processor 258 may process (e.g.,demodulate and decode) the detected symbols, provide decoded data forthe UE 120 to a data sink 260, and provide decoded control informationand system information to a controller/processor 280. A channelprocessor may determine reference signal received power (RSRP), receivedsignal strength indicator (RSSI), reference signal received quality(RSRQ), channel quality indicator (CQI), and/or the like. In someaspects, one or more components of the UE 120 may be included in ahousing.

On the uplink, at the UE 120, a transmit processor 264 may receive andprocess data from a data source 262 and control information (e.g., forreports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) from thecontroller/processor 280. Transmit processor 264 may also generatereference symbols for one or more reference signals. The symbols fromthe transmit processor 264 may be precoded by a TX MIMO processor 266 ifapplicable, further processed by modulators 254 a through 254 r (e.g.,for DFT-s-OFDM, CP-OFDM, and/or the like), and transmitted to the basestation 110. At the base station 110, the uplink signals from the UE 120and other UEs may be received by the antennas 234, processed by thedemodulators 254, detected by a MIMO detector 236 if applicable, andfurther processed by a receive processor 238 to obtain decoded data andcontrol information sent by the UE 120. The receive processor 238 mayprovide the decoded data to a data sink 239 and the decoded controlinformation to a controller/processor 240. The base station 110 mayinclude communications unit 244 and communicate to the core network 130via the communications unit 244. The core network 130 may include acommunications unit 294, a controller/processor 290, and a memory 292.

The controller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform one or more techniques associated a protocol stackfor analog transmission and reception in a split architecture network,as described in more detail elsewhere. For example, thecontroller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform or direct operations of, for example, the processesof FIGS. 15 and 16 and/or other processes as described. Memories 242 and282 may store data and program codes for the base station 110 and UE120, respectively. A scheduler 246 may schedule UEs for datatransmission on the downlink and/or uplink.

In some aspects, the UE 120 may include means for receiving, means fortransmitting, means for determining, and/or means for communicating. Insome aspects, the base station 110 may include means for receiving,means for transmitting, means for determining, and/or means forcommunicating. Such means may include one or more components of the UE120 or base station 110 described in connection with FIG. 2 .

As indicated above, FIG. 2 is provided merely as an example. Otherexamples may differ from what is described with regard to FIG. 2 .

In some cases, different types of devices supporting different types ofapplications and/or services may coexist in a cell. Examples ofdifferent types of devices include UE handsets, customer premisesequipment (CPEs), vehicles, Internet of Things (IoT) devices, and/or thelike. Examples of different types of applications include ultra-reliablelow-latency communications (URLLC) applications, massive machine-typecommunications (mMTC) applications, enhanced mobile broadband (eMBB)applications, vehicle-to-anything (V2X) applications, and/or the like.Furthermore, in some cases, a single device may support differentapplications or services simultaneously.

FIG. 3 illustrates an example implementation of a system-on-a-chip (SOC)300, which may include a central processing unit (CPU) 302 or amulti-core CPU configured for generating gradients for neural networktraining, in accordance with certain aspects of the present disclosure.The SOC 300 may be included in the base station 110 or UE 120. Variables(e.g., neural signals and synaptic weights), system parametersassociated with a computational device (e.g., neural network withweights), delays, frequency bin information, and task information may bestored in a memory block associated with a neural processing unit (NPU)308, in a memory block associated with a CPU 302, in a memory blockassociated with a graphics processing unit (GPU) 304, in a memory blockassociated with a digital signal processor (DSP) 306, in a memory block318, or may be distributed across multiple blocks. Instructions executedat the CPU 302 may be loaded from a program memory associated with theCPU 302 or may be loaded from a memory block 318.

The SOC 300 may also include additional processing blocks tailored tospecific functions, such as a GPU 304, a DSP 306, a connectivity block310, which may include fifth generation (5G) connectivity, fourthgeneration long term evolution (4G LTE) connectivity, Wi-Ficonnectivity, USB connectivity, Bluetooth connectivity, and the like,and a multimedia processor 312 that may, for example, detect andrecognize gestures. In one implementation, the NPU is implemented in theCPU, DSP, and/or GPU. The SOC 300 may also include a sensor processor314, image signal processors (ISPs) 316, and/or navigation module 320,which may include a global positioning system.

The SOC 300 may be based on an ARM instruction set. In an aspect of thepresent disclosure, the instructions loaded into the general-purposeprocessor 302 may comprise code to receive a control message from amachine learning training block or machine learning inference block. Theinstructions may also comprise code to transmit, to a network device,the control message via a digital data communications stack. Theinstructions may further comprise code to receive gradient data forfederated learning, from the machine learning training block or themachine learning inference block. The instructions may still furthercomprise code to determine whether to transmit the gradient data via ananalog data communications stack or the digital data communicationsstack based on a network configuration. The instructions may stillfurther comprise code to transmit, to the network device, the gradientdata via the analog data communications stack or the digital datacommunications stack in accordance with the determining.

In other aspect of the present disclosure, the instructions loaded intothe general-purpose processor 302 may comprise code to receive a controlmessage from a machine learning training block or machine learninginference block. The instructions may also comprise code to transmit thecontrol message via a digital data communications stack. Theinstructions may further comprise code to receive gradient data forfederated learning, from the machine learning training block or themachine learning inference block. The instructions may still furthercomprise code to determine whether to transmit the gradient data via ananalog data communications stack or the digital data communicationsstack based on a network configuration. The instructions may stillfurther comprise code to transmit the gradient data via the analog datacommunications stack or the digital data communications stack inaccordance with the determining.

Deep learning architectures may perform an object recognition task bylearning to represent inputs at successively higher levels ofabstraction in each layer, thereby building up a useful featurerepresentation of the input data. In this way, deep learning addresses amajor bottleneck of traditional machine learning. Prior to the advent ofdeep learning, a machine learning approach to an object recognitionproblem may have relied heavily on human engineered features, perhaps incombination with a shallow classifier. A shallow classifier may be atwo-class linear classifier, for example, in which a weighted sum of thefeature vector components may be compared with a threshold to predict towhich class the input belongs. Human engineered features may betemplates or kernels tailored to a specific problem domain by engineerswith domain expertise. Deep learning architectures, in contrast, maylearn to represent features that are similar to what a human engineermight design, but through training. Furthermore, a deep network maylearn to represent and recognize new types of features that a humanmight not have considered.

A deep learning architecture may learn a hierarchy of features. Ifpresented with visual data, for example, the first layer may learn torecognize relatively simple features, such as edges, in the inputstream. In another example, if presented with auditory data, the firstlayer may learn to recognize spectral power in specific frequencies. Thesecond layer, taking the output of the first layer as input, may learnto recognize combinations of features, such as simple shapes for visualdata or combinations of sounds for auditory data. For instance, higherlayers may learn to represent complex shapes in visual data or words inauditory data. Still higher layers may learn to recognize common visualobjects or spoken phrases.

Deep learning architectures may perform especially well when applied toproblems that have a natural hierarchical structure. For example, theclassification of motorized vehicles may benefit from first learning torecognize wheels, windshields, and other features. These features may becombined at higher layers in different ways to recognize cars, trucks,and airplanes.

Neural networks may be designed with a variety of connectivity patterns.In feed-forward networks, information is passed from lower to higherlayers, with each neuron in a given layer communicating to neurons inhigher layers. A hierarchical representation may be built up insuccessive layers of a feed-forward network, as described above. Neuralnetworks may also have recurrent or feedback (also called top-down)connections. In a recurrent connection, the output from a neuron in agiven layer may be communicated to another neuron in the same layer. Arecurrent architecture may be helpful in recognizing patterns that spanmore than one of the input data chunks that are delivered to the neuralnetwork in a sequence. A connection from a neuron in a given layer to aneuron in a lower layer is called a feedback (or top-down) connection. Anetwork with many feedback connections may be helpful when therecognition of a high-level concept may aid in discriminating theparticular low-level features of an input.

The connections between layers of a neural network may be fullyconnected or locally connected. FIG. 4A illustrates an example of afully connected neural network 402. In a fully connected neural network402, a neuron in a first layer may communicate its output to everyneuron in a second layer, so that each neuron in the second layer willreceive input from every neuron in the first layer. FIG. 4B illustratesan example of a locally connected neural network 404. In a locallyconnected neural network 404, a neuron in a first layer may be connectedto a limited number of neurons in the second layer. More generally, alocally connected layer of the locally connected neural network 404 maybe configured so that each neuron in a layer will have the same or asimilar connectivity pattern, but with connections strengths that mayhave different values (e.g., 410, 412, 414, and 416). The locallyconnected connectivity pattern may give rise to spatially distinctreceptive fields in a higher layer, because the higher layer neurons ina given region may receive inputs that are tuned through training to theproperties of a restricted portion of the total input to the network.

One example of a locally connected neural network is a convolutionalneural network. FIG. 4C illustrates an example of a convolutional neuralnetwork 406. The convolutional neural network 406 may be configured suchthat the connection strengths associated with the inputs for each neuronin the second layer are shared (e.g., 408). Convolutional neuralnetworks may be well suited to problems in which the spatial location ofinputs is meaningful.

One type of convolutional neural network is a deep convolutional network(DCN). FIG. 4D illustrates a detailed example of a DCN 400 designed torecognize visual features from an image 426 input from an imagecapturing device 430, such as a car-mounted camera. The DCN 400 of thecurrent example may be trained to identify traffic signs and a numberprovided on the traffic sign. Of course, the DCN 400 may be trained forother tasks, such as identifying lane markings or identifying trafficlights.

The DCN 400 may be trained with supervised learning. During training,the DCN 400 may be presented with an image, such as the image 426 of aspeed limit sign, and a forward pass may then be computed to produce anoutput 422. The DCN 400 may include a feature extraction section and aclassification section. Upon receiving the image 426, a convolutionallayer 432 may apply convolutional kernels (not shown) to the image 426to generate a first set of feature maps 418. As an example, theconvolutional kernel for the convolutional layer 432 may be a 5×5 kernelthat generates 28x28 feature maps. In the present example, because fourdifferent feature maps are generated in the first set of feature maps418, four different convolutional kernels were applied to the image 426at the convolutional layer 432. The convolutional kernels may also bereferred to as filters or convolutional filters.

The first set of feature maps 418 may be subsampled by a max poolinglayer (not shown) to generate a second set of feature maps 420. The maxpooling layer reduces the size of the first set of feature maps 418.That is, a size of the second set of feature maps 420, such as 14×14, isless than the size of the first set of feature maps 418, such as 28×28.The reduced size provides similar information to a subsequent layerwhile reducing memory consumption. The second set of feature maps 420may be further convolved via one or more subsequent convolutional layers(not shown) to generate one or more subsequent sets of feature maps (notshown).

In the example of FIG. 4D, the second set of feature maps 420 isconvolved to generate a first feature vector 424. Furthermore, the firstfeature vector 424 is further convolved to generate a second featurevector 428. Each feature of the second feature vector 428 may include anumber that corresponds to a possible feature of the image 426, such as“sign,” “60,” and “100.” A softmax function (not shown) may convert thenumbers in the second feature vector 428 to a probability. As such, anoutput 422 of the DCN 400 is a probability of the image 426 includingone or more features.

In the present example, the probabilities in the output 422 for “sign”and “60” are higher than the probabilities of the others of the output422, such as “30,” “40,” “50,” “70,” “80,” “90,” and “100”. Beforetraining, the output 422 produced by the DCN 400 is likely to beincorrect. Thus, an error may be calculated between the output 422 and atarget output. The target output is the ground truth of the image 426(e.g., “sign” and “60”). The weights of the DCN 400 may then be adjustedso the output 422 of the DCN 400 is more closely aligned with the targetoutput.

To adjust the weights, a learning algorithm may compute a gradientvector for the weights. The gradient may indicate an amount that anerror would increase or decrease if the weight were adjusted. At the toplayer, the gradient may correspond directly to the value of a weightconnecting an activated neuron in the penultimate layer and a neuron inthe output layer. In lower layers, the gradient may depend on the valueof the weights and on the computed error gradients of the higher layers.The weights may then be adjusted to reduce the error. This manner ofadjusting the weights may be referred to as “back propagation” as itinvolves a “backward pass” through the neural network.

In practice, the error gradient of weights may be calculated over asmall number of examples, so that the calculated gradient approximatesthe true error gradient. This approximation method may be referred to asstochastic gradient descent. Stochastic gradient descent may be repeateduntil the achievable error rate of the entire system has stoppeddecreasing or until the error rate has reached a target level. Afterlearning, the DCN may be presented with new images (e.g., the speedlimit sign of the image 426) and a forward pass through the network mayyield an output 422 that may be considered an inference or a predictionof the DCN.

Deep belief networks (DBNs) are probabilistic models comprising multiplelayers of hidden nodes. DBNs may be used to extract a hierarchicalrepresentation of training data sets. A DBN may be obtained by stackingup layers of Restricted Boltzmann Machines (RBMs). An RBM is a type ofartificial neural network that can learn a probability distribution overa set of inputs. Because RBMs can learn a probability distribution inthe absence of information about the class to which each input should becategorized, RBMs are often used in unsupervised learning. Using ahybrid unsupervised and supervised paradigm, the bottom RBMs of a DBNmay be trained in an unsupervised manner and may serve as featureextractors, and the top RBM may be trained in a supervised manner (on ajoint distribution of inputs from the previous layer and target classes)and may serve as a classifier.

Deep convolutional networks (DCNs) are networks of convolutionalnetworks, configured with additional pooling and normalization layers.DCNs have achieved state-of-the-art performance on many tasks. DCNs canbe trained using supervised learning in which both the input and outputtargets are known for many exemplars and are used to modify the weightsof the network by use of gradient descent methods.

DCNs may be feed-forward networks. In addition, as described above, theconnections from a neuron in a first layer of a DCN to a group ofneurons in the next higher layer are shared across the neurons in thefirst layer. The feed-forward and shared connections of DCNs may beexploited for fast processing. The computational burden of a DCN may bemuch less, for example, than that of a similarly sized neural networkthat comprises recurrent or feedback connections.

The processing of each layer of a convolutional network may beconsidered a spatially invariant template or basis projection. If theinput is first decomposed into multiple channels, such as the red,green, and blue channels of a color image, then the convolutionalnetwork trained on that input may be considered three-dimensional, withtwo spatial dimensions along the axes of the image and a third dimensioncapturing color information. The outputs of the convolutionalconnections may be considered to form a feature map in the subsequentlayer, with each element of the feature map (e.g., 220) receiving inputfrom a range of neurons in the previous layer (e.g., feature maps 218)and from each of the multiple channels. The values in the feature mapmay be further processed with a non-linearity, such as a rectification,max(0, x). Values from adjacent neurons may be further pooled, whichcorresponds to down sampling, and may provide additional localinvariance and dimensionality reduction. Normalization, whichcorresponds to whitening, may also be applied through lateral inhibitionbetween neurons in the feature map.

The performance of deep learning architectures may increase as morelabeled data points become available or as computational powerincreases. Modem deep neural networks are routinely trained withcomputing resources that are thousands of times greater than what wasavailable to a typical researcher just fifteen years ago. Newarchitectures and training paradigms may further boost the performanceof deep learning. Rectified linear units may reduce a training issueknown as vanishing gradients. New training techniques may reduceover-fitting and thus enable larger models to achieve bettergeneralization. Encapsulation techniques may abstract data in a givenreceptive field and further boost overall performance.

FIG. 5 is a block diagram illustrating a deep convolutional network 550.The deep convolutional network 550 may include multiple different typesof layers based on connectivity and weight sharing. As shown in FIG. 5 ,the deep convolutional network 550 includes the convolution blocks 554A,554B. Each of the convolution blocks 554A, 554B may be configured with aconvolution layer (CONV) 356, a normalization layer (LNorm) 558, and amax pooling layer (MAX POOL) 560.

The convolution layers 556 may include one or more convolutionalfilters, which may be applied to the input data to generate a featuremap. Although only two of the convolution blocks 554A, 554B are shown,the present disclosure is not so limiting, and instead, any number ofthe convolution blocks 554A, 554B may be included in the deepconvolutional network 550 according to design preference. Thenormalization layer 558 may normalize the output of the convolutionfilters. For example, the normalization layer 558 may provide whiteningor lateral inhibition. The max pooling layer 560 may provide downsampling aggregation over space for local invariance and dimensionalityreduction.

The parallel filter banks, for example, of a deep convolutional networkmay be loaded on a CPU 302 or GPU 304 of an SOC 300 to achieve highperformance and low power consumption. In alternative embodiments, theparallel filter banks may be loaded on the DSP 306 or an ISP 316 of anSOC 300. In addition, the deep convolutional network 550 may accessother processing blocks that may be present on the SOC 300, such assensor processor 314 and navigation module 320, dedicated, respectively,to sensors and navigation.

The deep convolutional network 550 may also include one or more fullyconnected layers 562 (FC1 and FC2). The deep convolutional network 550may further include a logistic regression (LR) layer 564. Between eachlayer 556, 558, 560, 562, 564 of the deep convolutional network 550 areweights (not shown) that are to be updated. The output of each of thelayers (e.g., 556, 558, 560, 562, 564) may serve as an input of asucceeding one of the layers (e.g., 556, 558, 560, 562, 564) in the deepconvolutional network 550 to learn hierarchical feature representationsfrom input data 552 (e.g., images, audio, video, sensor data and/orother input data) supplied at the first of the convolution blocks 554A.The output of the deep convolutional network 550 is a classificationscore 566 for the input data 552. The classification score 566 may be aset of probabilities, where each probability is the probability of theinput data, including a feature from a set of features.

As indicated above, FIGS. 3-5 are provided as examples. Other examplesmay differ from what is described with respect to FIGS. 3-5 .

Some machine learning approaches centralize training data on onemachine, or in a data center. A federated learning model supportscollaborative learning of a shared prediction model among user equipment(UEs) and a base station (or centralized server). Federated learning isa process where a group of UEs receives a machine learning model from abase station and work together to train the model. More specifically,each UE trains the model locally, and sends back either updated neuralnetwork model weights or gradient updates from, for example, a locallyperformed stochastic gradient descent process. The base station receivesthe updates from all of the UEs in the group and aggregates the updates,for example, by averaging the updates, to obtain updated global weightsof the neural network. The base station sends the updated model to theUEs, and the process repeats, round after round, until a desiredperformance level from the global model is obtained.

Over the air (OTA) aggregation for federated learning is an attractiveapproach due to its low communication overhead. With OTA aggregation,each UE transmits the gradient of the weights to the network (e.g.,parameter server). The network then aggregates the gradients from all ofthe UEs according to a function (e.g., a summation). Because wirelesssignals naturally add up on the uplink, it is beneficial to transmit thegradient of each parameter on the uplink as analog data for each usersimultaneously. Thus, the base station (e.g., gNB) receives the sum ofthe gradients in the ideal scenario. However, considerations such aspower control, fading compensation, phase correction, etc., need to beaddressed so that the base station properly adds the gradients.

FIG. 6 is a block diagram illustrating over the air aggregation forfederated learning, in accordance with aspects of the presentdisclosure. In the example of FIG. 6 , the variable Θ_(k) represents thegradient of the parameter of interest for the k^(th) user participatingin the aggregation process. A network 602 receives a gradient from eachof the K users, and aggregates the gradients in accordance with afunction. The network 602 may communicate with a multiple accesschannel, such as an orthogonal frequency-division multiple access(OFDMA) channel. In the example of FIG. 6 , a summation is implemented.The overall gradient Y is a sum of each individually received gradientΘ_(k) plus some noise n. The overall gradient Y may be returned to eachof the K users at the edge devices.

FIG. 7 is a block diagram illustrating a network architecture, inaccordance with aspects of the present disclosure. In the examplearchitecture of FIG. 7 , a core network (CN) 130 communicates with basestations 110, which may be functionally split into a centralized unit(CU) 702, distributed units (DUs) 704 and radio units (RUs) 706. Each CU702 communicates with the core network 130 via a backhaul connection.The CU 702 communicates with each of the DUs 704 via an F1 interface.The DU 704 is for managing the radio link control (RLC) layer, the mediaaccess control (MAC) layer, and parts of the physical (PHY) layer of thebase station 110. The DU 704 communicates with each of the RUs 706 via afronthaul connection, which may be an ORAN fronthaul interface (OFI).The RUs 706 of the base station 110 manage the digital front end andparts of the PHY layer for communicating wirelessly with the UEs 120.The RUs 706 each communicate with multiple UEs 120 via a physicaldownlink shared channel (PDSCH) and physical uplink shared channel(PUSCH), for example.

A given neural network has many weights, often millions of weights. Onlya subset of weights may need to be updated in each round of a federatedlearning process. For example, UEs may communicate neural networkweights or gradients among themselves via an over the air (OTA) link.The gradient/weight data may be aggregated at a symbol level with theOTA link. Thus, channel noise and fading are considered when sharingupdates. Similarly, the RUs 706 aggregate the gradient/weight data atthe symbol level, while also addressing channel noise and fading. At theDU level, aggregation occurs at the packet level. The data aggregationis coupled with the RU level because UEs 120 are scheduled by the DUs704. At the CU level, data is aggregated at the packet level. Becausethe UEs 120 may be in idle mode, connected mode, or cell search mode,the CU level is coupled with the DU aggregation and the RU aggregation.The core network (CN) 130 may aggregate data received from multiple basestations 110 at the highest level. As more data becomes available,heterogeneity of the gradient/weight decreases.

As noted above, only a subset of weights may need to be updated in eachround of a federated learning process. For example, initial featurelayers may be fixed while final layers may be updated. FIG. 8 is a blockdiagram illustrating distribution of a neural network across variousnetwork elements, in accordance with aspects of the present disclosure.The neural network has five layers in the example of FIG. 8 . A firstlayer resides in a core network (CN) 130. A second layer resides in acentralized unit (CU) 702. A third layer resides in a distributed unit(DU) 704. Fourth and fifth layers reside in a radio unit (RU) 706. Insome scenarios, weights for some of the layers may be fixed, whileweights in other layers may be updated. For example, layer one may betrained for all UEs in all of the distributed units 704 (only one shown)associated with all centralized units 702 (only one shown) associatedwith a given core network 130. In another example, layer two is trainedfor all UEs of all distributed units 704 associated with a givencentralized unit 702. In a third example, layer three is trained for allUEs in all radio units 706 associated with a given distributed unit 704.In a fourth example, layers four and five are trained for all UEsassociated with a given radio unit 706. These weights at layers four andfive may be affected by operating conditions at the base station.

In other scenarios, weights for some layers may be updated frequently,while weights for other layers may be updated at a slower rate. Forexample, layers four and five may be updated once every 100 ms, whilelayers two and three may update once every 1000 ms. In each of thesescenarios, not all gradients are needed at each time occasion forupdating and for network operation. The network should be able to signalwhich layers and weights are requested.

FIG. 9 is a block diagram illustrating an architecture for a networkmanaged machine learning model of a device, in accordance with aspectsof the present disclosure. The base station 110 may include a number oflogical entities, such as the CU 702, at least one DU 704 (only oneshown) and at least one radio unit (RU) 706 (only one shown.) The CU 702includes a centralized unit control plane (CU-CP) 904 for managing theradio resource control (RRC) layer and packet data convergence protocol(PDCP) layer of the base station 110. The CU 702 also includes acentralized unit user plane (CU-UP) 906 for managing the user plane partof the PDCP layer and the user plane part of the service data adaptationprotocol (SDAP) layer. The CU 702 further includes a centralized unitmachine learning plane (CU-XP) 908 for managing machine learningfunctions, such as which model to select to execute a neural networkfunction. The CU-CP 904, CU-UP 906, and CU-XP 908 communicate with eachother via an E1 interface. While the CU-XP 908 is illustrated in FIG. 9as being part of the base station 110, separate from the CU-CP 904, theCU-XP 908 may alternatively be implemented as part of the CU-CP 904 oras (a portion of) a network entity separate from the base station 110.

Various entities within the network may manage artificial intelligence(AI) or machine learning (ML) functions. For example, the CU-XP 908 mayconfigure and manage device AI/ML network assisted functions. RANnetwork elements (such as the CU-CP 904, CU-UP 906, and DU 704), as wellas the device itself (e.g., the UE 120), may host online inference andmodel training functions that are configured and managed by the CU-XP908. A UE model repository or centralized unit model repository(UE/CU-MR) 902 may store and retrieve machine learning models fortraining or inference used by the UE 120 or network entities, such asthe CU 702, the DU 704, or a radio access network (RAN) intelligentcontroller (RIC) (not shown).

As described, with federated learning techniques, different sets ofanalog gradient data may be fetched at different times. It would bedesirable to introduce a framework for realizing the federated learningtechniques with a new radio (NR) network. Currently, the NR network isdesigned to process data packets and not analog inputs from the physicallayer (PHY). Aspects of the present disclosure introduce anarchitectural enhancement to the NR network to enable the network tocompute uplink channel-based analog gradient sums. The protocolarchitecture includes an analog protocol stack and a digital protocolstack (also referred to as analog data communications stack and digitaldata communications stack, respectively). The analog protocol stack maytransport analog data such as machine learning model gradients orweights.

FIG. 10 is a block diagram illustrating a protocol stack for processingmachine learning data, in accordance with aspects of the presentdisclosure. The protocol stack includes multiple layers that communicatewith each other. The layers may be software or hardware-based. Each UE120 (e.g., UE 1 to UE N) includes a protocol stack, as does each basestation 110. A machine learning (ML) layer 1002 is present in theprotocol stacks of the UE 120, the base station 110, and the corenetwork 130. The ML layer 1002 may be tasked with managing machinelearning data transmission and reception needs of the network entitiesfor transmissions to and from the base station 110, the UE 120, and thecore network 130. An interface exists between machine learning trainingand inference blocks, data management and control blocks, etc. (notshown) at one end of the ML layer 1002. An interface also exists betweenthe ML layer 1002 and the UE and base station transmission and receptionstacks at the other end. The core network 130 includes an ML layer 1002that communicates with the ML layer 1002 of the base station 110 toreceive the gradient data.

According to aspects of the present disclosure, the ML layer 1002 may beconfigured to transmit the data it handles in either an analog ordigital format. For the digital format, the data passes through theregular UE digital data stack, for example, a service data adaptationprotocol (SDAP) layer 1004, a packet data convergence protocol (PDCP)layer 1006, a radio link control (RLC) layer 1008, a media accesscontrol (MAC) layer 1010, and a physical (PHY) layer 1012. For theanalog format, the data passes through a simplified UE analog stackcontaining an upper analog MAC (MAC-A) layer 1014, a lower analog MAClayer 1016, and an analog physical (PHY-A) layer 1018. Although FIG. 10show a separate UE analog stack including the upper analog MAC layer1014, the lower analog MAC layer 1016, and the analog physical layer1018, the functions of these layers may be performed by the regular UEdata stack. For example, the functions of the upper analog MAC layer1014 and lower analog MAC layer 1016 may be performed by the MAC layer1010. Similarly, the functions of the analog physical layer 1018 may beperformed by the physical layer 1012.

According to aspects of the present disclosure, based on either a priorconfiguration or a dynamic indication, the ML layer 1002 can choose totransmit data in either digital or analog format. The decision may be apacket-by-packet determination of a flow-by-flow determination. In someaspects, all control messages may be transmitted using the digital datastack. Gradient data to be transmitted for the purpose of federatedlearning may be transmitted in either the analog format or the digitalformat. If the network determines that there are a limited number of UEscommunicating with a particular base station participating in thefederated learning session, the network may decide it is more efficientto transmit gradient data in the digital domain. Alternatively, if thenetwork determines that a large number of UEs are participating in thefederated learning session, then the network may decide it is moreefficient to transmit gradient data in the analog domain. In case thefunctions of the analog layers are performed by the regular UE datastack, the ML layer 1002 communicates with the regular UE digital datastack with an indication of whether the analog or digital functionsshould be performed either packet-by-packet or flow-by-flow.

According to aspects of the present disclosure, the upper analog MAClayer 1014 is the holding entity for the data for each neural networkto/from the ML layer 1002. The upper analog MAC layer 1014 may handlesome higher order functions, such as analog ciphering, bearermanagement, and packet management (e.g., discarding, etc.). In theanalog domain, ciphering may include a sign change or multiplying by aphase, in the case of complex numbers.

According to aspects of the present disclosure, the lower MAC-A layer1016 may perform segmentation and reassembly of analog data, mapping tothe correct component carrier, handling lower layer retransmissions,etc. The lower MAC-A layer 1016 may also multiplex and de-multiplexgradient data from multiple neural networks when appropriatelyconfigured.

According to aspects of the present disclosure, the PHY-A layer 1018 maybe configured to transmit and receive analog data (e.g., real andcomplex numbers) by taking gradient data and mapping the gradient datato real numbers. The PHY-A layer 1018 may also perform bandwidthexpansion and contraction, for example, with techniques such asrepetition, puncturing, interleaving, etc. The PHY-A layer 1018 may alsomap the data to the transmit waveform and de-map data from the receivewaveform, etc. The PHY-A layer 1018 may implement the actual transmitscheme, such as mapping to the correct transmit port, using thedetermined beamforming and pre-equalization, maintaining phasecoherence, etc.

According to further aspects of the present disclosure, radio resourcecontrol (RRC) messages may provide an overall high-level configurationfor operating the upper and lower MAC-A layers 1014, 1016, and the PHY-Alayer 1018. The configuration may be semi-static or long term. In someaspects, the lower MAC-A layer 1016 may receive control messages, suchas media access control-control element (MAC-CE) commands, from the MAClayer 1010 in the digital stack. These MAC-CE commands may have variouspurposes, such as dynamic control and configuration of various lowerMAC-A and PHY-A related functions.

According to still further aspects of the present disclosure, the PHY-Alayer 1018 processes either an analog downlink or analog uplink sharedchannel (PUSCH-A and PDSCH-A). The PUSCH-A is a channel for carryinguplink gradients for federated learning. The PDSCH-A can carry gradientsfrom the base station 110 to the UE 120, if it is determined that analogtransmission in the downlink is beneficial.

As part of a federated learning process, gradients are transmitted fromthe UEs to the network. Some exemplary gradient transmission call flowsare now described.

FIG. 11 is a call flow diagram illustrating network-initiated gradienttransmission, in accordance with aspects of the present disclosure. Inthe example of FIG. 11 , a machine learning layer 1102 of the UE 120communicates with lower layers of the UE stack 1104, as well as with amachine learning (ML) layer 1108 of the base station 110 (also referredto as the gNB ML layer). The lower layers of the UE stack 1104 includethe SDAP layer 1004, PDCP layer 1006, RLC layer 1008, MAC layer 1010,and PHY layer 1012 of the digital stack, and the upper MAC-A layer 1014,lower MAC-A layer 1016, and the PHY-A layer 1018 of the analog stack.Although not illustrated in the example of FIG. 11 , the ML layer 1108of the base station 110 can also include the ML layer of the corenetwork 130. Lower layers of a base station stack 1106 (also referred toas the gNB stack) include the SDAP layer 1004, PDCP layer 1006, RLClayer 1008, MAC layer 1010, and PHY layer 1012 of the digital stack, andthe upper MAC-A layer 1014, lower MAC-A layer 1016, and the PHY-A layer1018 of the analog stack.

In the example of FIG. 11 , the network (e.g., base station 110 or corenetwork 130) initiates gradient computing at the UE 120 for each roundof a federated learning process. More specifically, at time t 1, thebase station ML layer 1108 establishes a federated learning session withthe UE ML layer 1102. At time t 2, the base station ML layer 1108transmits a message to the UE ML layer 1102 initiating gradientcomputation at the UE and providing configuration information. With thismessage, the base station 110 requests the UE 120 to collect data tostart gradient computation. The message may indicate what layer of thenetwork data is to be collected for, how much data to collect, whichneural network to train, etc.

In response, the UE 120 collects data samples and computes gradients,which are transferred to the UE ML layer 1102 for storage in a buffer(e.g., at the upper MAC-A layer 1014). At time t 3, the UE ML layer 1102transmits the gradient data to the UE stack 1104 once the gradients arecomputed after data collection is complete. At this moment, the basestation 110 is not aware of whether the gradients are ready at the UE120. For example, different UEs may have different processing times dueto different processing power, a different number of samples, etc. Thus,at time t 4, the UE stack 1104 transmits a message informing the basestation stack 1106 that gradient data is available at the UE 120. Thebase station stack 1106 forwards the message to the base station MLlayer 1108 at time t 5.

The base station 110 receives messages from multiple UEs 120 indicatingavailability of data. Once the base station 110 receives this messagefrom all UEs 120 participating in the federated learning process, orafter a threshold amount of time elapses, the base station ML layer 1108transmits a message initiating gradient transfer at time t 6. At time t7, the base station stack 1106 transmits configuration information andgrants for the UE 120 to transmit the gradient data. The grants may beDCI-based, MAC-CE-based or RRC-based. Depending on the grant size andamount of gradient data, the UE 120 may segment the data to fit intoeach PHY layer grant. The configuration information may also indicatewhether gradients should be compressed, whether gradients should betransmitted in an analog or digital format, etc. The message may providethe physical layer resources for the gradient transmission, as well as aPHY layer coding scheme, etc. This message may not occur in this time inthe call flow. The information may be sent earlier as well.

At time t 8, the UE stack 1104 transfers the gradient data to the basestation stack 1106 via the PUSCH-A or PUSCH depending on whether thegradient data is in the analog or digital format. At time t 9, the UEstack 1104 may transmit a PUSCH message for any other relatedinformation, such as how much data has been transmitted, whether anygrants have been missed, sequence numbers, etc. If additional data isexpected, at time t 10, the base station stack 1106 transmits additionalgrants to the UE stack 1104. At times t 11 and t 12, the UE stack 1104transmits gradient data and any additional information via PUSCH and/orPUSCH-A messages. The steps at times t 11 and t 12 repeat until allgradients have been transmitted.

After the base station stack 1106 has properly received all the gradientdata, the base station stack 1106 forwards the gradient data to the basestation ML layer 1108 at time t 13. At time 114, the base station MLlayer 1108 transmits an acknowledgement (ACK) to the base station stack1106, which forwards a message to the UE stack 1104 instructing flushingof the UE buffer storing the gradient data at time 115. Upon receivingconfirmation from the network, the UE 120 can flush its buffer.

If the network determines the quality of the gradient data is inadequateor determines that more data is needed, the network may initiate a fullor partial retransmission with appropriate messages to the base stationstack 1106. If this is the case, at time t 16, the base station ML layer1108 transmits a message to the UE ML layer 1102 initiating gradienttransfer and providing configuration information. At time t 17, the UEML layer 1102 transfers the gradient data to the UE stack 1104 and theprocess repeats as described with respect to times t 3 to t 15.

As described above, transmission of gradient data can be in an analog ordigital format. In some aspects of the present disclosure, informationabout the format is provided at the start of the session. In otheraspects, the format is determined dynamically. For example, a firsttransmission can be analog and any retransmissions (e.g., due to misseddownlink control information (DCIs)) can be in a digital format. If thedata is in a digital format, the UE ML layer or PHY layer can optionallycompress the gradient data for digital transmission. A compressionmethod may be specified in a standard or specified by configuration.

The nature of data sources used for computing gradients may vary. Forexample, some sources of underlying data can be regular, such asperiodic or quasiperiodic, while other sources are irregular, such asevent-based data. A time specified for a gradient computation may dependon a nature of the data source. In some aspects of the presentdisclosure, the UE informs the network when gradient data is availablefor a configured number of samples.

FIG. 12A is a call flow diagram illustrating network-initiated datasample availability reporting, in accordance with aspects of the presentdisclosure. FIG. 12B is a call flow diagram illustrating UE-initiateddata sample availability reporting, in accordance with aspects of thepresent disclosure. In the example of FIGS. 12A and 12B, a machinelearning (ML) layer 1102 of the UE 120 communicates with lower layers ofthe UE stack 1104, as well as with a machine learning layer 1108 of thebase station 110. The lower layers of the UE stack 1104 include the SDAPlayer 1004, PDCP layer 1006, RLC layer 1008, MAC layer 1010, and PHYlayer 1012 of the digital stack, and the upper MAC-A layer 1014, lowerMAC-A layer 1016, and the PHY-A layer 1018 of the analog stack. Althoughnot illustrated in the example of FIGS. 12A and 12B, the ML layer 1108of the base station 110 can also include the ML layer of the corenetwork 130. The lower layers of the base station stack 1106 include theSDAP layer 1004, PDCP layer 1006, RLC layer 1008, MAC layer 1010, andPHY layer 1012 of the digital stack, and the upper MAC-A layer 1014,lower MAC-A layer 1016, and the PHY-A layer 1018 of the analog stack.

In the example of FIG. 12A, the base station ML layer 1108 sets up asession with the UE ML layer 1102 at time t 1. To handle a variableamount of data samples for each UE 120, in some scenarios, the basestation 110 may query the UE 120 about the number of samples availableand request the UE 120 to compute the gradients using the availablesamples if the number is larger than a threshold. For example, at time t2, the base station ML layer 1108 may transmit a message to the UE MLlayer 1102 requesting the UE 120 to report the number of samplesavailable at the UE 120 for computing gradients. In some aspects, themessage may adjust the physical layer transmission parameters, such aspower control, to improve OTA gradient weighting. At time t 3, the UE MLlayer 1102 responds to the base station ML layer 1108 with a number ofdata samples available or whether a number of data samples exceeds athreshold value. The threshold value may be configured at time t 1 ortime t 2. If the base station 110 determines the number of samples issufficient, the base station ML layer 1108 may send a message to the UEML layer 1102 initiating gradient computation and providingconfiguration information at time t 4. That is, in the example of FIG.12A, the base station 110 does not wait for all samples to be collectedby the UE 120. The base station 110 initiates gradient computation aftera sufficient number of samples is collected.

In the example of FIG. 12B, the base station ML layer 1108 sets up asession with the UE ML layer 1102 at time t 1. At time t 2, the UE MLlayer 1102 reports to the base station ML layer 1108 when a number ofsamples is greater than a threshold. If the base station 110 determinesthe number of samples is sufficient, the base station ML layer 1108 maysend a message to the UE ML layer 1102 initiating gradient computationand providing configuration information at time t 3.

FIG. 13 is a call flow diagram illustrating continuous gradientcomputing at a UE, in accordance with aspects of the present disclosure.In the example of FIG. 13 , the base station 110 may fetch gradient datawhen needed. At time t 1, a base station ML layer 1108 establishes afederated learning session with a UE ML layer 1102. At time t 2, thebase station ML layer 1108 transmits a message to the UE ML layer 1102initiating gradient computation at the UE and providing configurationinformation. With this message, the base station 110 requests the UE 120to collect data to start gradient computation. The message may indicatewhat layer of the network data is to be collected for, how much data tocollect, which neural network to train, etc.

In response, the UE 120 collects data samples and computes gradients,which are transferred to the UE ML layer 1102. At time t 3, the UE MLlayer 1102 transmits the gradient data to a UE stack 1104. At time t 4,the UE stack 1104 transmits a message informing a base station stack1106 that gradient data is available at the UE 120. In this example,five data samples are available at time t 4. The base station stack 1106forwards the message to the base station ML layer 1108 at time t 5. Theprocess repeats after the UE 120 performs another round of computation.That is, at time t 6, the UE ML layer 1102 transmits the gradient datato the UE stack 1104. At time t 7, the UE stack 1104 transmits a messageinforming the base station stack 1106 that gradient data is available atthe UE 120. In this example, ten data samples are available at time t 7.The base station stack 1106 forwards the message to the base station MLlayer 1108 at time t 8.

Once the base station 110 receives the message from all UEs 120participating in the federated learning process, or after a thresholdamount of time elapses, the base station ML layer 1108 transmits amessage to the base station stack 1106 initiating gradient transfer attime t 9. In the example of FIG. 13 , the base station 110 may fetch thegradient when needed. In some aspects, the gradient data indicated asavailable at time t 7 is weight combined, for example, averaged, withthe gradient data available at time t 4. In other aspects, the gradientdata available at time t 7 replaces the gradient data available at timet 4.

More detail of each of the layers of the analog protocol stack will nowbe presented. For analog transmission, the output of the machinelearning layer to the upper MAC-A layer may be represented as a datapacket. FIG. 14 is a block diagram illustrating a data packet format forgradient transmissions, in accordance with aspects of the presentdisclosure. The data packet format enables lower layers to understandthe data being received from the upper MAC-A layer. In the example ofFIG. 14 , the data packet format has a main header 1402 and data fields1404. The main header 1402 contains at least a request ID (e.g., sessionID or sequence ID), a model ID for which the gradient data was requestedpresented, time stamps, number of samples used for gradient computation,and other header information to enable parsing of the packet. In someaspects, a single packet is associated with the gradients for eachneural network model.

According to aspects of the present disclosure, within the data fields1404 of each data packet, the weights of the neural network may belisted in sequential order or grouped by network layer and listed insequential order for each network layer. Each weight may be mapped to anindex. Indexing for this function may be provided by a neural networkdescription known to the upper layers. In some aspects, each gradientvalue is represented in binary notation in some known/standardizedformat (e.g., IEEE floating point, with a specified number of bits). Theformat may be configurable, for example, when the session is initiated.The layers may be configured with different resolutions. For example,some layers may specify a 16-bit format whereas other layers may specifya 12-bit format or eight-bit format. The formats may also vary basedupon weights. In some aspects, a per neural network layer number of bitsis configured for gradient representation. Referring back to FIG. 14 ,the data fields 1404 may include a header 1406 with the neural networklayer ID, followed by gradient data 1408 for that neural network layer.After the gradient data 1408 for the first layer, another header 1410indicates the next neural network layer, followed by gradient data 1412for this next layer.

The base station may request a specific set of gradients for a PUSCH-Atransmission. The request may include starting and ending neural networkweight indices. According to some aspects of the present disclosure, thelower MAC-A and PHY-A (lower layers in general) are aware of the packetstructure and can parse the packet received from the upper layer. Thus,the requested gradients may be extracted and encoded properly fortransmission. This behavior is the opposite of the digital stack, wherethe lower layer cannot parse the contents of the upper layer packet. Forexample, the MAC layer cannot parse an RLC packet and the RLC layercannot parse a PDCP, etc.

For the final transmission from the PHY-A layer, only the gradients aretransmitted. The headers 1402, 1406, 1410 are dropped. The headers 1402,1406, 1410 are provided for parsing and selecting the correct set ofweights. Headers are useful because there may be a neural networkspecific or neural network layer specific encoding to be performed. Forexample, some neural network layers can be transmitted with morerepetition but others with less, as the weights of some layers may be ofhigher importance.

Functions of the upper MAC-A layer may include analog ciphering. Forreal data, each gradient may have its sign updated based on a cipheroutput. The same ciphering should be implemented for all UEs so thegradient data can be deciphered at the base station correctly.

Functions of the upper MAC-A layer may include bearer management. Insome aspects, bearers may be managed by routing packets to differentcarriers or cell groups (e.g., a master cell group (MCG) or secondarycell group (SCG)). In other aspects, the upper MAC-A layer managesbearers by splitting packets within a bearer (e.g., a split bearer).Bearers may be split on a per neural network layer basis. For example,neural network layer one packets transmit to the MCG, and neural networklayer two packets may transmit to the SCG, etc.

The upper MAC-A layer may also perform packet acknowledgement and packetdiscarding. The upper MAC-A layer stores the packets. If the basestation acknowledges receipt of the gradients and/or instructs the UE toflush the gradient data, then the UE can flush the packet in the upperMAC-A. Alternatively, a timer-based discard can be implemented. Forexample, if data is older than some amount of network configured time(e.g., ten seconds) the data is too old and will be discarded. Ifmultiple gradient data packets are available for the same neuralnetwork, the upper MAC-A layer may implement sequence numbering so thebase station can receive the correct gradient data. For example, thefirst gradient may be transmitted as packet one, the second gradient aspacket two, etc.

The upper MAC-A layer may manage status reports. Status reports may besent to the base station via the digital data stack. Status may includeavailability or unavailability of gradient data from machine learninglayers, for example.

The lower MAC-A layer will now be discussed. A number of requestedgradients may be in the millions for large models. If one parameter (ortwo in case of complex transmission) is mapped onto one orthogonalfrequency division multiplexing (OFDM) subcarrier, it would take manyPHY slots to transmit the gradients for just one layer. For example, fora 100 MHz bandwidth and 14 symbols in frequency range one (FR1), the UEcan transmit 3264*14 ~ 45,000 parameters in one full slot. This suggeststhat the MAC layer needs to perform segmentation and reassembly ofgradient data, even within a layer when the number of gradients for eachneural network layer is large. In the opposite scenario, the MAC layercan multiplex the data from multiple neural network layers to match thePHY layer allocated resources.

According to aspects of the present disclosure, the lower MAC-A layerperforms segmentation and reassembly of data. Based on a resourceallocation received and a PHY layer coding scheme implemented, the MAC-Alayer may segment the packet to select the appropriate number ofgradients for transmitting. For example, in a slot n, the schedulerallocates a set of N_(tones)▪ N_(symbols) in a slot. The PHY layercoding scheme may include repetition, repetition plus sign/phase change,padding, puncturing, repetition plus interleaving, etc. Extra tones maybe allocated in each symbol to embed symbols and reduce peak-to-averagepower ratio (PAPR). Based on all these considerations, the lower MAC-Alayer determines N_(grad) values to transmit in the allocated grant. Agrant may also include a starting gradient index and the N_(grad) value.

According to further aspects of the disclosure, the network may transmitgrants, or reserve resources and activate them, using MAC-CEs. EachMAC-CE may contain all information needed to transmit by the PHY-Alayer. For example, the MAC-CE may include the slot ID, neural network(NN) ID, layer and weight indices, PHY-A layer encoding scheme, resourceallocation, analog or digital (re)-transmission, etc. In someconfigurations, all contents of the DCI may be sent in the MAC-CE. Thetransmission may be sequential or may be an indication-basedtransmission of gradients. The MAC-CE may be transmitted on the digitalstack on the PDSCH. As a result, the network receives an acknowledgement(ACK) when the UE receives the MAC-CE. On the other hand, if DCI wasused, no ACK would be sent. If the UE misses the PDSCH containing theMAC-CE, the network will not receive an ACK and may reschedule thePDSCH, improving reliability. If the network indicates digitaltransmission, then the packet is encoded by the MAC layer andtransmitted over the regular PUSCH. When the gradients are transmittedas analog data, no hybrid automatic repeat request (HARQ) processingoccurs. However, retransmissions may be requested.

According to still further aspects, if multiple neural network modelsare updated simultaneously, then the data from each model is keptdistinct. Each set of neural network model parameters may be stored inseparate upper MAC-A layer buffers. In some configurations, gradientsfrom multiple neural networks may be multiplexed into a singletransmission, when appropriately configured.

According to aspects of the present disclosure, the lower MAC-A layertransmits buffer status reports, as opposed to an upper layer statusreport. For example, if the upper UE layers indicate data is not ready,then this status may be sent to the base station in a MAC-CE. The MAC-CEmay carry the identity of the requesting message and possibly a reasonwhy data is not available. The MAC-CE is a digital message and is nottransmitted on the PUSCH-A.

According to further aspects of the present disclosure, the lower MAC-Alayer performs power headroom reporting. The power headroom reportsallow the base station to group UEs dynamically and adjust a set ofexisting grouped UEs, if needed. A new power headroom report for analogdata (PHR-A) is introduced. The conventional PHR is derived assuming areference PUSCH transmission over M resource blocks (RBs) (where M=1).In some configurations, the PHR-A report may be derived using a meantransmit power of the last N analog transmissions. In otherconfigurations, the UE may construct a potential PUSCH transmission of Nfuture slots assuming a reference number of resource blocks for eachslot. The UE then computes the average power required to transmit them.This value is indicated in the PHR-A. A distribution of the gradientdata should be specified so that the UE can derive samples to betransmitted on each symbol and then compute the peak-to-average powerratio (PAPR), compute the mean transmit power and backoff needed, andthen derive the PHR-A.

The PHY-A layer will now be described. The downlink (DL) PHY-A layer atthe UE receives a DCI grant (e.g., a group grant) from the base stationand sends information to the MAC-A layers to receive a set of weights tobe transmitted. The PHY-A layer may receive a downlink-analog modulationreference signal (DL-AMRS) spanning all resources used for uplinktransmission. Based on the DL-AMRS, the PHY-A layer may compute a pathloss, a channel for pre-equalization/pre-processing, phase correction,timing correction, etc., needed for uplink transmission. Alternatively,path loss may be derived from other reference signals, such as asynchronization signal block (SSB)/channel state information-referencesignal (CSI-RS) and applied with a correction factor.

Uplink PHY-A functions at the UE include receiving parameters from theMAC-A layers and implementing coding, interleaving, etc., within a slot.The uplink PHY-A layer creates a real/complex number from the parametersand maps the real/complex numbers onto OFDM symbols and subcarriers. Theuplink (UL) PHY-A layer also implements power control, pre-equalization,phase correction, timing correction, etc. The UE may implementadditional gradient scaling (e.g., as indicated by the grant or asindicated by a number of samples used by upper layers in the header).The UE may then transmit the time domain waveform. The UE may alsotransmit an UL-AMRS to enable the base station to determine whether agiven UE is transmitting. In some aspects, the base station may transmita new DL-AMRS for pre-equalization every time a phase change occurs atthe UE, such as with a downlink/uplink switch or bandwidth part (BWP)retuning, etc.

According to aspects of the present disclosure, an uplink grant may bereceived in downlink control information (DCI) information elements,similar to information elements for the MAC-CE-based grants. A firstexample DCI format includes a neural network model ID, a layer ID, aweights starting index, and a number of weights, which enables per layeraddressing of weights. A second example DCI format includes a neuralnetwork model ID, a weights starting index, and a number of weights,which enables global addressing of weights across layers. Additionalinformation may include a BWP ID, a cell ID, time and frequencyresources, a value for a k 2 (delay) parameter, scaling/power controlvalues, AMRS configuration parameters, sounding reference signal (SRS)configuration parameters, interleaving information, a port ID, etc. Thek 2 delay parameter indicates a delay between a slot N during which agrant is received and a slot N+k2 for transmitting the gradients. A listof weights for each layer may be provided by the upper layers at thetime of federated learning configuration.

The PHY-A layer may also process downlink grant reception. According toaspects of the present disclosure, a group DCI format may be definedwith a new radio network temporary identifier (RNTI). A payload size maybe determined by upper layer configuration based on a number of fieldsand a size of each field. The downlink grant may map to an existingsearch space or a newly defined search space. Upon receiving thephysical downlink control channel (PDCCH), the UE may be configured totransmit an ACK to the base station. Parameters for this ACK may beconfigured by RRC signaling. The ACK may be transmitted on a physicaluplink control channel (PUCCH) or multiplexed with another UCI messageon the PUSCH. The ACK informs the base station of whether the UEreceived the PDCCH. The UE may not transmit for other reasons, such as ahandover started, insufficient transmit power, etc., however, the basestation knows the downlink grant has been received by the UE. Anindividual grant with the same DCI format (e.g., with a different RNTIor using a cell RNTI (C-RNTI)) may also be defined. Thus, the basestation may request the UE to transmit individually, as well.

According to further aspects of the present disclosure, MAC-CE orRRC-based transmission triggering may provide another type of grant. Inthese aspects, a same grant payload may be transmitted in a MAC-CE or byan RRC message on a PDSCH or a multi-cast PDSCH for multiple UEs. Theseaspects allow for more complex grants with multiple repetitions, as wellas requests for multiple layers bundled into the same message.

According to further aspects of the present disclosure, an RRCconfiguration may describe details of each scheme, such as coding,pre-equalization, etc.

In other aspects, an antenna port used to receive the DL-AMRS andtransmit the uplink gradients is the same. Thus, the channel estimatedon the downlink for pre-equalization is the same as the channelexperienced by the uplink transmission.

The PHY-A layer may implement robustness schemes to improve reliabilityof transmissions of the analog data. For example, the PHY-A layer maycommunicate with time/frequency/spatial diversity for analogtransmission. More robustness schemes are applicable to MIMO scenarios.These schemes may include cyclic delay diversity (CDD), beamforming,etc.

FIG. 15 is a block diagram illustrating PHY layer repetition withdifferent antenna ports, in accordance with aspects of the presentdisclosure. In the example of FIG. 15 , a first DCI message 1502provides a grant for a first PUSCH-A 1504 in a single input, singleoutput (SISO) scenario. The first grant is for the first one thousandweights of layer one of neural network (NN) one. The first grantindicates port one for transmitting with interleaving type one. A secondDCI message 1506 provides a grant for a second PUSCH-A 1508. The secondgrant is also for the first one thousand weights of layer one of neuralnetwork (NN) one. The second grant indicates port two for transmittingwith interleaving type one. This scheme of varying the port fortransmitting the same data may be referred to as repetition coding, asthe PUSCH-A is transmitted on orthogonal resources on both occasions.

FIG. 16 is a block diagram illustrating PHY layer repetition withdifferent interleaving, in accordance with aspects of the presentdisclosure. In the example of FIG. 16 , a first DCI message 1602provides a grant for a first PUSCH-A 1604 in a single input, singleoutput (SISO) scenario. The first grant is for the first one thousandweights of layer one of neural network (NN) one. The first grantindicates port one for transmitting with interleaving type one. A secondDCI message 1606 provides a grant for a second PUSCH-A 1608. The secondgrant is also for the first one thousand weights of layer one of neuralnetwork (NN) one. The second grant indicates port one for transmittingwith interleaving type two. This scheme of varying the interleavingtechnique for transmitting the same data may also be referred to asrepetition coding, as the PUSCH-A is transmitted on orthogonal resourceson both occasions.

Although the present disclosure has been described with respect tofederated learning, the disclosure is not so limited. For example, thedescribed techniques may have applicability in amplify and forwardrelays, smart repeaters, smart reconfigurable intelligent surfaces(RIS), etc., where it is also beneficial to support analog transmissionschemes. Moreover, although gradients have been described, the presentdisclosure also contemplates sharing weights instead of or in additionto the gradients.

FIG. 17 is a flow diagram illustrating an example process 1700performed, for example, by a user equipment (UE), in accordance withvarious aspects of the present disclosure. The example process 1700 maybe used with a protocol stack for analog transmission and reception in asplit architecture network for machine learning (ML) functions. Theoperations of the process 1700 may be implemented by a UE 120.

At block 1702, the user equipment (UE) receives a control message from amachine learning training block or machine learning inference block. Forexample, the UE (e.g., using the antenna 252, DEMOD/MOD 254, MIMOdetector 256, receive processor 258, controller/processor 280, memory282 and/or the like) may receive the control message. At block 1704, theuser equipment (UE) transmits, to a network device, the control messagevia a digital data communications stack. For example, the UE (e.g.,using the antenna 252, DEMOD/MOD 254, TX MIMO processor 266, transmitprocessor 264, controller/processor 280, memory 282 and/or the like) maytransmit the control message.

At block 1706, the user equipment (UE) receives gradient data forfederated learning, from the machine learning training block or themachine learning inference block. For example, the UE (e.g., using theantenna 252, DEMOD/MOD 254, MIMO detector 256, receive processor 258,controller/processor 280, memory 282 and/or the like) may receive thegradient data. At block 1708, the user equipment (UE) determines whetherto transmit the gradient data via an analog data communications stack orthe digital data communications stack based on a network configuration.For example, the UE (e.g., using the controller/processor 280, memory282 and/or the like) may determine whether to transmit the gradient databased on a prior configuration or a dynamic indication. Thedetermination may be packet-by-packet or stream-by-stream. In someaspects, the network configuration may be based on a number of UEsparticipating in the federated learning.

At block 1710, the user equipment (UE) transmits, to the network device,the gradient data via the analog data communications stack or thedigital data communications stack in accordance with the determining.For example, the UE (e.g., using the antenna 252, DEMOD/MOD 254, TX MIMOprocessor 266, transmit processor 264, controller/processor 280, memory282 and/or the like) may transmit the gradient data. In some aspects,the transmitting is via an analog physical uplink shared channel(PUSCH-A). In other aspects, the transmitting is via an analog functionwithin a digital physical uplink shared channel (PUSCH).

FIG. 18 is a flow diagram illustrating an example process 1800performed, for example, by a network device, in accordance with variousaspects of the present disclosure. The example process 1800 is anexample of process performed with a protocol stack for analogtransmission and reception in a split architecture network for machinelearning (ML) functions. The operations of the process 1800 may beimplemented by a network device, such as a base station 110.

At block 1802, the network device receives a control message from amachine learning training block or machine learning inference block. Forexample, the base station (e.g., using the antenna 234, MOD/DEMOD 232,MIMO detector 236, receive processor 238, controller/processor 240,memory 242 and/or the like) may receive the control message. At block1804, the network device transmits the control message via a digitaldata communications stack. For example, the base station (e.g., usingthe antenna 234, MOD/DEMOD 232, TX MIMO processor 230, transmitprocessor 220, controller/processor 240, memory 242 and/or the like) maytransmit the control message.

At block 1806, the network device receives gradient data for federatedlearning, from the machine learning training block or the machinelearning inference block. For example, the base station (e.g., using theantenna 234, MOD/DEMOD 232, MIMO detector 236, receive processor 238,controller/processor 240, memory 242 and/or the like) may receive thegradient data. At block 1808, the network device determines whether totransmit the gradient data via an analog data communications stack orthe digital data communications stack based on a network configuration.For example, the base station (e.g., using the controller/processor 240,memory 242 and/or the like) may determine whether to transmit thegradient data. The determination may be packet-by-packet orstream-by-stream. In some aspects, the network configuration may bebased on a number of UEs participating in the federated learning.

At block 1810, the network device transmits the gradient data via theanalog data communications stack or the digital data communicationsstack in accordance with the determining. For example, the base station(e.g., using the antenna 234, MOD/DEMOD 232, TX MIMO processor 230,transmit processor 220, controller/processor 240, memory 242 and/or thelike) may transmit the gradient data. In some aspects, the transmittingis via an analog physical uplink shared channel (PUSCH-A). In otheraspects, the transmitting is via an analog function within a digitalphysical uplink shared channel (PUSCH).

EXAMPLE ASPECTS

Aspect 1: A protocol stack architecture for processing machine learningdata at a user equipment (UE), comprising: a machine learning layerconfigured to manage communication of machine learning data with anetwork device, the machine learning layer coupled to a plurality ofmachine learning training blocks, and machine learning and inferenceblocks for a plurality of neural networks; an analog data communicationsstack coupled to the machine learning layer and comprising: an uppermedia access control analog (MAC-A) layer coupled to the machinelearning layer and configured to store data for each of the plurality ofneural networks; a lower MAC-A layer coupled to the upper MAC-A layerand configured to segment and reassemble analog machine learning data;and an analog physical layer coupled to the lower MAC-A layer andconfigured to transmit and receive analog data to and from the networkdevice; and

a digital data communications stack coupled to the machine learninglayer and the lower MAC-A layer and configured to manage digitalcommunications with the network device.

Aspect 2: The architecture of Aspect 1, in which the upper MAC-A layeris further configured for analog ciphering of the analog data, bearermanagement for the analog data, and packet management for the analogdata.

Aspect 3: The architecture of Aspect 1 or 2, in which the lower MAC-Alayer is further configured to map the analog data to componentcarriers, to manage data retransmission for the analog data, and toperform multiplexing and de-multiplexing of the analog data from theplurality of neural networks.

Aspect 4: The architecture of any of the preceding Aspects, in which theanalog physical layer is further configured to control a bandwidth sizefor the analog data, to map to a transmit waveform, to de-map from areceive waveform, to map the analog data to transmit ports, and tomanage beamforming, pre-equalization and phase coherence.

Aspect 5: The architecture of any of the preceding Aspects, in which thelower MAC-A layer is further configured to receive control messages froma MAC layer of the digital data communications stack.

Aspect 6: The architecture of any of the preceding Aspects, in which thecontrol messages dynamically configure the analog data communicationstack.

Aspect 7: The architecture of any of the preceding Aspects, in whichfunctions of the analog data communication stack are performed by thedigital data communications stack.

Aspect 8: A method of wireless communication, by a user equipment (UE),comprising: receiving a control message from a machine learning trainingblock or machine learning inference block; transmitting, to a networkdevice, the control message via a digital data communications stack;receiving gradient data for federated learning, from the machinelearning training block or the machine learning inference block;determining whether to transmit the gradient data via an analog datacommunications stack or the digital data communications stack based on anetwork configuration; and transmitting, to the network device, thegradient data via the analog data communications stack or the digitaldata communications stack in accordance with the determining.

Aspect 9: The method of Aspect 8, further comprising receiving thenetwork configuration dynamically.

Aspect 10: The method of Aspects 8 or 9, further comprising receivingthe network configuration via radio resource control signaling.

Aspect 11: The method of any of the Aspects 8-10, in which the networkconfiguration is pre-configured.

Aspect 12: The method of any of the Aspects 8-11, in which thedetermining is performed packet-by-packet.

Aspect 13: The method of any of the Aspects 8-11, in which thedetermining is performed stream-by-stream.

Aspect 14: The method of any of the Aspects 8-10 and 12-13, in which thenetwork configuration is based on a quantity of UEs participating in thefederated learning.

Aspect 15: The method of any of the Aspects 8-14, in which thetransmitting is via an analog physical uplink shared channel (PUSCH-A).

Aspect 16: The method of any of the Aspects 8-15, further comprisingreceiving, from the network device, neural network weights and/or thegradient data via an analog physical downlink shared channel (PDSCH-A).

Aspect 17: The method of any of the Aspects 8-16, further comprisingreceiving, from the network device, the neural network weights and/orthe gradient data via a broadcast channel.

Aspect 18: The method of any of the Aspects 8-17, in which transmittingvia the analog data communications stack comprises communicating withthe digital data communications stack with an indication that analogfunctions should be performed.

Aspect 19: A protocol stack architecture for processing machine learningdata at a network device, comprising: a machine learning layerconfigured to manage communication of machine learning data with a userequipment (UE), the machine learning layer coupled to a plurality ofmachine learning training blocks, and machine learning and inferenceblocks for a plurality of neural networks; an analog data communicationsstack coupled to the machine learning layer and comprising: an uppermedia access control analog (MAC-A) layer coupled to the machinelearning layer and configured to store data for each of the plurality ofneural networks; a lower MAC-A layer coupled to the upper MAC-A layerand configured to segment and reassemble analog machine learning data;and an analog physical layer coupled to the lower MAC-A layer andconfigured to transmit and receive analog data to and from the networkdevice; and a digital data communications stack coupled to the machinelearning layer and the lower MAC-A layer and configured to managedigital communications with the UE.

Aspect 20: The architecture of Aspect 19, in which the upper MAC-A layeris further configured for analog ciphering of the analog data, bearermanagement for the analog data, and packet management for the analogdata.

Aspect 21: The architecture of Aspect 19 or 20, in which the lower MAC-Alayer is further configured to map the analog data to componentcarriers, to manage data retransmission for the analog data, and toperform multiplexing and de-multiplexing of the analog data from theplurality of neural networks.

Aspect 22: The architecture of any of the Aspects 19-21, in which theanalog physical layer is further configured to control a bandwidth sizefor the analog data, to map to a transmit waveform, to de-map from areceive waveform, to map the analog data to transmit ports, and tomanage beamforming, pre-equalization and phase coherence.

Aspect 23: The architecture of any of the Aspects 19-22, in which thelower MAC-A layer is further configured to receive control messages froma MAC layer of the digital data communications stack.

Aspect 24: The architecture of any of the Aspects 19-23, in which thecontrol messages dynamically configure the analog data communicationstack.

Aspect 25: The architecture of any of the Aspects 19-24, in whichfunctions of the analog data communication stack are performed by thedigital data communications stack.

Aspect 26: A method of wireless communication, by a network device,comprising: receiving a control message from a machine learning trainingblock or machine learning inference block; transmitting the controlmessage via a digital data communications stack; receiving gradient datafor federated learning, from the machine learning training block or themachine learning inference block; determining whether to transmit thegradient data via an analog data communications stack or the digitaldata communications stack based on a network configuration; andtransmitting the gradient data via the analog data communications stackor the digital data communications stack in accordance with thedetermining.

Aspect 27: The method of Aspect 26, further comprising transmittingneural network weights and/or the gradient data via an analog physicaldownlink shared channel (PDSCH-A).

Aspect 28: The method of Aspect 26 or 27, further comprisingtransmitting the neural network weights and/or the gradient data via abroadcast channel.

Aspect 29: The method of any of the Aspects 26-28, in which the networkconfiguration is pre-configured.

Aspect 30: The method of any of the Aspects 26-29, in which transmittingvia the analog data communications stack comprises communicating withthe digital data communications stack with an indication that analogfunctions should be performed.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the aspects to the preciseform disclosed. Modifications and variations may be made in light of theabove disclosure or may be acquired from practice of the aspects.

As used, the term “component” is intended to be broadly construed ashardware, firmware, and/or a combination of hardware and software. Asused, a processor is implemented in hardware, firmware, and/or acombination of hardware and software.

Some aspects are described in connection with thresholds. As used,satisfying a threshold may, depending on the context, refer to a valuebeing greater than the threshold, greater than or equal to thethreshold, less than the threshold, less than or equal to the threshold,equal to the threshold, not equal to the threshold, and/or the like.

It will be apparent that systems and/or methods described may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the aspects. Thus, the operation and behavior of thesystems and/or methods were described without reference to specificsoftware code—it being understood that software and hardware can bedesigned to implement the systems and/or methods based, at least inpart, on the description.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various aspects. In fact, many ofthese features may be combined in ways not specifically recited in theclaims and/or disclosed in the specification. Although each dependentclaim listed below may directly depend on only one claim, the disclosureof various aspects includes each dependent claim in combination withevery other claim in the claim set. A phrase referring to “at least oneof” a list of items refers to any combination of those items, includingsingle members. As an example, “at least one of: a, b, or c” is intendedto cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combinationwith multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c,a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering ofa, b, and c).

No element, act, or instruction used should be construed as critical oressential unless explicitly described as such. Also, as used, thearticles “a” and “an” are intended to include one or more items, and maybe used interchangeably with “one or more.” Furthermore, as used, theterms “set” and “group” are intended to include one or more items (e.g.,related items, unrelated items, a combination of related and unrelateditems, and/or the like), and may be used interchangeably with “one ormore.” Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used, the terms “has,” “have,” “having,”and/or the like are intended to be open-ended terms. Further, the phrase“based on” is intended to mean “based, at least in part, on” unlessexplicitly stated otherwise.

What is claimed is:
 1. A protocol stack architecture for processingmachine learning data at a user equipment (UE), comprising: a machinelearning layer configured to manage communication of machine learningdata with a network device, the machine learning layer coupled to aplurality of machine learning training blocks, and machine learning andinference blocks for a plurality of neural networks; an analog datacommunications stack coupled to the machine learning layer andcomprising: an upper media access control analog (MAC-A) layer coupledto the machine learning layer and configured to store data for each ofthe plurality of neural networks; a lower MAC-A layer coupled to theupper MAC-A layer and configured to segment and reassemble analogmachine learning data; and an analog physical layer coupled to the lowerMAC-A layer and configured to transmit and receive analog data to andfrom the network device; and a digital data communications stack coupledto the machine learning layer and the lower MAC-A layer and configuredto manage digital communications with the network device.
 2. Thearchitecture of claim 1, in which the upper MAC-A layer is furtherconfigured for analog ciphering of the analog data, bearer managementfor the analog data, and packet management for the analog data.
 3. Thearchitecture of claim 1, in which the lower MAC-A layer is furtherconfigured to map the analog data to component carriers, to manage dataretransmission for the analog data, and to perform multiplexing andde-multiplexing of the analog data from the plurality of neuralnetworks.
 4. The architecture of claim 1, in which the analog physicallayer is further configured to control a bandwidth size for the analogdata, to map to a transmit waveform, to de-map from a receive waveform,to map the analog data to transmit ports, and to manage beamforming,pre-equalization and phase coherence.
 5. The architecture of claim 1, inwhich the lower MAC-A layer is further configured to receive controlmessages from a MAC layer of the digital data communications stack. 6.The architecture of claim 5, in which the control messages dynamicallyconfigure the analog data communication stack.
 7. The architecture ofclaim 1, in which functions of the analog data communication stack areperformed by the digital data communications stack.
 8. A method ofwireless communication, by a user equipment (UE), comprising: receivinga control message from a machine learning training block or machinelearning inference block; transmitting, to a network device, the controlmessage via a digital data communications stack; receiving gradient datafor federated learning, from the machine learning training block or themachine learning inference block; determining whether to transmit thegradient data via an analog data communications stack or the digitaldata communications stack based on a network configuration; andtransmitting, to the network device, the gradient data via the analogdata communications stack or the digital data communications stack inaccordance with the determining.
 9. The method of claim 8, furthercomprising receiving the network configuration dynamically.
 10. Themethod of claim 9, further comprising receiving the networkconfiguration via radio resource control signaling.
 11. The method ofclaim 8, in which the network configuration is pre-configured.
 12. Themethod of claim 8, in which the determining is performedpacket-by-packet.
 13. The method of claim 8, in which the determining isperformed stream-by-stream.
 14. The method of claim 8, in which thenetwork configuration is based on a quantity of UEs participating in thefederated learning.
 15. The method of claim 8, in which the transmittingis via an analog physical uplink shared channel (PUSCH-A).
 16. Themethod of claim 8, further comprising receiving, from the networkdevice, neural network weights and/or the gradient data via an analogphysical downlink shared channel (PDSCH-A).
 17. The method of claim 16,further comprising receiving, from the network device, the neuralnetwork weights and/or the gradient data via a broadcast channel. 18.The method of claim 8, in which transmitting via the analog datacommunications stack comprises communicating with the digital datacommunications stack with an indication that analog functions should beperformed.
 19. A protocol stack architecture for processing machinelearning data at a network device, comprising: a machine learning layerconfigured to manage communication of machine learning data with a userequipment (UE), the machine learning layer coupled to a plurality ofmachine learning training blocks, and machine learning and inferenceblocks for a plurality of neural networks; an analog data communicationsstack coupled to the machine learning layer and comprising: an uppermedia access control analog (MAC-A) layer coupled to the machinelearning layer and configured to store data for each of the plurality ofneural networks; a lower MAC-A layer coupled to the upper MAC-A layerand configured to segment and reassemble analog machine learning data;and an analog physical layer coupled to the lower MAC-A layer andconfigured to transmit and receive analog data to and from the networkdevice; and a digital data communications stack coupled to the machinelearning layer and the lower MAC-A layer and configured to managedigital communications with the UE.
 20. The architecture of claim 19, inwhich the upper MAC-A layer is further configured for analog cipheringof the analog data, bearer management for the analog data, and packetmanagement for the analog data.
 21. The architecture of claim 19, inwhich the lower MAC-A layer is further configured to map the analog datato component carriers, to manage data retransmission for the analogdata, and to perform multiplexing and de-multiplexing of the analog datafrom the plurality of neural networks.
 22. The architecture of claim 19,in which the analog physical layer is further configured to control abandwidth size for the analog data, to map to a transmit waveform, tode-map from a receive waveform, to map the analog data to transmitports, and to manage beamforming, pre-equalization and phase coherence.23. The architecture of claim 19, in which the lower MAC-A layer isfurther configured to receive control messages from a MAC layer of thedigital data communications stack.
 24. The architecture of claim 23, inwhich the control messages dynamically configure the analog datacommunication stack.
 25. The architecture of claim 19, in whichfunctions of the analog data communication stack are performed by thedigital data communications stack.
 26. A method of wirelesscommunication, by a network device, comprising: receiving a controlmessage from a machine learning training block or machine learninginference block; transmitting the control message via a digital datacommunications stack; receiving gradient data for federated learning,from the machine learning training block or the machine learninginference block; determining whether to transmit the gradient data viaan analog data communications stack or the digital data communicationsstack based on a network configuration; and transmitting the gradientdata via the analog data communications stack or the digital datacommunications stack in accordance with the determining.
 27. The methodof claim 26, further comprising transmitting neural network weightsand/or the gradient data via an analog physical downlink shared channel(PDSCH-A).
 28. The method of claim 27, further comprising transmittingthe neural network weights and/or the gradient data via a broadcastchannel.
 29. The method of claim 26, in which the network configurationis pre-configured.
 30. The method of claim 26, in which transmitting viathe analog data communications stack comprises communicating with thedigital data communications stack with an indication that analogfunctions should be performed.